A Transcriptomic Biomarker to Classify Chemical-Induced Histone Deacetylase Inhibition in Human HepaRG Cells
RNA-seq (gene expression) · human · 477 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 477 samples: 78 conditions, mostly with about 4 replicates eachGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE342302_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1507318 and SRA study SRP313167. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 308 samples.
How we read this: 78 replicated groups read from the first 400 of 477 sample titles; they account for 308 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM5224163 HCC3_ZDV_C5_001 HCC3_ZDV_C5
- GSM5224164 HCC3_ZDV_C5_002 HCC3_ZDV_C5
- GSM5224165 HCC3_ZDV_C5_003 HCC3_ZDV_C5
- GSM5224166 HCC3_ZDV_C5_004 HCC3_ZDV_C5
- GSM5224167 HCC3_MMS_C5_005 HCC3_MMS_C5
- GSM5224168 HCC3_MMS_C5_006 HCC3_MMS_C5
- GSM5224169 HCC3_MMS_C5_007 HCC3_MMS_C5
- GSM5224170 HCC3_MMS_C5_008 HCC3_MMS_C5
- GSM5224171 HCC3_ZDV_C4_009 HCC3_ZDV_C4
- GSM5224172 HCC3_ZDV_C4_010 HCC3_ZDV_C4
- GSM5224173 HCC3_ZDV_C4_011 HCC3_ZDV_C4
- GSM5224174 HCC3_ZDV_C4_012 HCC3_ZDV_C4
- GSM5224175 HCC3_MMS_C4_013 HCC3_MMS_C4
- GSM5224176 HCC3_MMS_C4_014 HCC3_MMS_C4
- GSM5224177 HCC3_MMS_C4_015 HCC3_MMS_C4
- GSM5224178 HCC3_MMS_C4_016 HCC3_MMS_C4
- GSM5224179 HCC3_ZDV_C3_017 HCC3_ZDV_C3
- GSM5224180 HCC3_ZDV_C3_018 HCC3_ZDV_C3
- GSM5224181 HCC3_ZDV_C3_019 HCC3_ZDV_C3
- GSM5224182 HCC3_ZDV_C3_020 HCC3_ZDV_C3
- GSM5224183 HCC3_MMS_C3_021 HCC3_MMS_C3
- GSM5224184 HCC3_MMS_C3_022 HCC3_MMS_C3
- GSM5224185 HCC3_MMS_C3_023 HCC3_MMS_C3
- GSM5224186 HCC3_MMS_C3_024 HCC3_MMS_C3
- GSM5224187 HCC3_ZDV_C2_025 HCC3_ZDV_C2
- GSM5224188 HCC3_ZDV_C2_026 HCC3_ZDV_C2
- GSM5224189 HCC3_ZDV_C2_027 HCC3_ZDV_C2
- GSM5224190 HCC3_ZDV_C2_028 HCC3_ZDV_C2
- GSM5224191 HCC3_MMS_C2_029 HCC3_MMS_C2
- GSM5224192 HCC3_MMS_C2_030 HCC3_MMS_C2
- GSM5224193 HCC3_MMS_C2_031 HCC3_MMS_C2
- GSM5224194 HCC3_MMS_C2_032 HCC3_MMS_C2
- GSM5224195 HCC3_ZDV_C1_033 HCC3_ZDV_C1
- GSM5224196 HCC3_ZDV_C1_034 HCC3_ZDV_C1
- GSM5224197 HCC3_ZDV_C1_035 HCC3_ZDV_C1
- GSM5224198 HCC3_ZDV_C1_036 HCC3_ZDV_C1
- GSM5224199 HCC3_MMS_C1_037 HCC3_MMS_C1
- GSM5224200 HCC3_MMS_C1_038 HCC3_MMS_C1
- GSM5224201 HCC3_MMS_C1_039 HCC3_MMS_C1
- GSM5224202 HCC3_MMS_C1_040 HCC3_MMS_C1
+ 437 more — browse all 477 samples with per-sample file links →
- GSE199913 KDM4C inhibition blocks tumor growth in basal breast cancer by promoting cathepsin L-mediated histone H3 cleavage 231 samples shares inhibition, histone
- GSE319266 Identification of target genes and regulatory networks for bone mineral density GWAS loci through systematic targeting and inhibition [hFOB BMD STING-seq] 24 samples shares inhibition
- GSE283522 The spatially informed mFISHseq assay resolves biomarker discordance and predicts treatment response in breast cancer 1254 samples shares biomarker
- GSE287540 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [bulk RNA-seq] 128 samples shares biomarker
- GSE287541 Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury [scRNA-seq] 92 samples shares biomarker
- GSE328930 The DUTRENEO trial: bulk prospective biomarker-guided therapy selection and spatial transcriptomic dissection of immunotherapy response in muscle-invasive bladder cancer 66 samples shares biomarker
- GSE319887 Friedreich’s ataxia transcriptomic dysregulation and identification of cell type-specific biomarkers: A systematic review and meta-analysis 43 samples shares biomarker
- GSE236442 Combined analysis of metabolomics, proteomics, and transcriptomics to identify biomarkers for resistant hypertension 30 samples shares biomarker
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.